Context: Over the past years, variational autoencoders (VAEs) have proven efficient for generative modeling of complicated signals, e.g. speech and audio [1]. Recently, they have successfully been applied to audio-visual speech separation (AVSS) [2], where the goal is to separate a target speech from a mixture of several speech signals,…
Context: Deep latent variable models (DLVMs) provide an effective way to model the underlying hidden generative process of natural signals and images [1]. This allows us to approximate the probability density functions of data which in turn can be used for either generating new examples resembling training data or do…
In many audio-visual applications, e.g., speech enhancement and speech recognition, it is desirable to have aligned images of the mouth region such that a deep neural network can extract reliable visual features. Indeed, the quality of the extracted visual features impacts the performance of audio-visual based applications. In reality, however,…
